



from argparse import ArgumentParser, Namespace

from ..utils import logging
from . import BaseTransformersCLICommand






def  lowerCamelCase_	(      lowerCAmelCase__	:	Namespace				)       ->							Dict:

							'''simple docstring'''






							return ConvertCommand(
							    args.model_type			,	args.tf_checkpoint			,	args.pytorch_dump_output			,	args.config			,	args.finetuning_task_name				)


__snake_case     :Tuple			       ='\ntransformers can only be used from the commandline to convert TensorFlow models in PyTorch, In that case, it requires\nTensorFlow to be installed. Please see https://www.tensorflow.org/install/ for installation instructions.\n'



class        lowerCAmelCase__					(  _lowerCamelCase       ):


				@staticmethod
				def __UpperCamelCase						(							__UpperCamelCase      :  ArgumentParser       )		->     Optional[int]:
											A      						=    parser.add_parser(
											    'convert'						, help='CLI tool to run convert model from original author checkpoints to Transformers PyTorch checkpoints.'						, )
											train_parser.add_argument('--model_type'						, type=__UpperCamelCase						, required=__UpperCamelCase						, help='Model\'s type.'       )
											train_parser.add_argument(
											    '--tf_checkpoint'						, type=__UpperCamelCase						, required=__UpperCamelCase						, help='TensorFlow checkpoint path or folder.'       )
											train_parser.add_argument(
											    '--pytorch_dump_output'						, type=__UpperCamelCase						, required=__UpperCamelCase						, help='Path to the PyTorch saved model output.'       )
											train_parser.add_argument('--config'						, type=__UpperCamelCase						, default=''						, help='Configuration file path or folder.'       )
											train_parser.add_argument(
											    '--finetuning_task_name'						, type=__UpperCamelCase						, default=__UpperCamelCase						, help='Optional fine-tuning task name if the TF model was a finetuned model.'						, )
											train_parser.set_defaults(func=__UpperCamelCase       )


				def __init__(							self      :  List[str]						, __UpperCamelCase      :  str						, __UpperCamelCase      :  str						, __UpperCamelCase      :  str						, __UpperCamelCase      :  str						, __UpperCamelCase      :  str						, *__UpperCamelCase      :  Dict						, )		->     Optional[int]:
											A      						=    logging.get_logger('transformers-cli/converting'       )

											self._logger.info(f'''Loading model {model_type}'''       )
											A      						=    model_type
											A      						=    tf_checkpoint
											A      						=    pytorch_dump_output
											A      						=    config
											A      						=    finetuning_task_name


				def __UpperCamelCase						(							self      :  Optional[int]       )		->     Tuple:
											if self._model_type == "albert":
																		try:
																									from ..models.albert.convert_albert_original_tf_checkpoint_to_pytorch import (
																									    convert_tf_checkpoint_to_pytorch,
																									)
																		except ImportError:
																									raise ImportError(__UpperCamelCase       )

																		convert_tf_checkpoint_to_pytorch(self._tf_checkpoint						, self._config						, self._pytorch_dump_output       )
											elif self._model_type == "bert":
																		try:
																									from ..models.bert.convert_bert_original_tf_checkpoint_to_pytorch import (
																									    convert_tf_checkpoint_to_pytorch,
																									)
																		except ImportError:
																									raise ImportError(__UpperCamelCase       )

																		convert_tf_checkpoint_to_pytorch(self._tf_checkpoint						, self._config						, self._pytorch_dump_output       )
											elif self._model_type == "funnel":
																		try:
																									from ..models.funnel.convert_funnel_original_tf_checkpoint_to_pytorch import (
																									    convert_tf_checkpoint_to_pytorch,
																									)
																		except ImportError:
																									raise ImportError(__UpperCamelCase       )

																		convert_tf_checkpoint_to_pytorch(self._tf_checkpoint						, self._config						, self._pytorch_dump_output       )
											elif self._model_type == "t5":
																		try:
																									from ..models.ta.convert_ta_original_tf_checkpoint_to_pytorch import convert_tf_checkpoint_to_pytorch
																		except ImportError:
																									raise ImportError(__UpperCamelCase       )

																		convert_tf_checkpoint_to_pytorch(self._tf_checkpoint						, self._config						, self._pytorch_dump_output       )
											elif self._model_type == "gpt":
																		from ..models.openai.convert_openai_original_tf_checkpoint_to_pytorch import (
																		    convert_openai_checkpoint_to_pytorch,
																		)

																		convert_openai_checkpoint_to_pytorch(self._tf_checkpoint						, self._config						, self._pytorch_dump_output       )
											elif self._model_type == "transfo_xl":
																		try:
																									from ..models.transfo_xl.convert_transfo_xl_original_tf_checkpoint_to_pytorch import (
																									    convert_transfo_xl_checkpoint_to_pytorch,
																									)
																		except ImportError:
																									raise ImportError(__UpperCamelCase       )

																		if "ckpt" in self._tf_checkpoint.lower():
																									A      						=    self._tf_checkpoint
																									A      						=    ''
																		else:
																									A      						=    self._tf_checkpoint
																									A      						=    ''
																		convert_transfo_xl_checkpoint_to_pytorch(
																		    __UpperCamelCase						, self._config						, self._pytorch_dump_output						, __UpperCamelCase       )
											elif self._model_type == "gpt2":
																		try:
																									from ..models.gpta.convert_gpta_original_tf_checkpoint_to_pytorch import (
																									    convert_gpta_checkpoint_to_pytorch,
																									)
																		except ImportError:
																									raise ImportError(__UpperCamelCase       )

																		convert_gpta_checkpoint_to_pytorch(self._tf_checkpoint						, self._config						, self._pytorch_dump_output       )
											elif self._model_type == "xlnet":
																		try:
																									from ..models.xlnet.convert_xlnet_original_tf_checkpoint_to_pytorch import (
																									    convert_xlnet_checkpoint_to_pytorch,
																									)
																		except ImportError:
																									raise ImportError(__UpperCamelCase       )

																		convert_xlnet_checkpoint_to_pytorch(
																		    self._tf_checkpoint						, self._config						, self._pytorch_dump_output						, self._finetuning_task_name       )
											elif self._model_type == "xlm":
																		from ..models.xlm.convert_xlm_original_pytorch_checkpoint_to_pytorch import (
																		    convert_xlm_checkpoint_to_pytorch,
																		)

																		convert_xlm_checkpoint_to_pytorch(self._tf_checkpoint						, self._pytorch_dump_output       )
											elif self._model_type == "lxmert":
																		from ..models.lxmert.convert_lxmert_original_tf_checkpoint_to_pytorch import (
																		    convert_lxmert_checkpoint_to_pytorch,
																		)

																		convert_lxmert_checkpoint_to_pytorch(self._tf_checkpoint						, self._pytorch_dump_output       )
											elif self._model_type == "rembert":
																		from ..models.rembert.convert_rembert_tf_checkpoint_to_pytorch import (
																		    convert_rembert_tf_checkpoint_to_pytorch,
																		)

																		convert_rembert_tf_checkpoint_to_pytorch(self._tf_checkpoint						, self._config						, self._pytorch_dump_output       )
											else:
																		raise ValueError(
																		    '--model_type should be selected in the list [bert, gpt, gpt2, t5, transfo_xl, xlnet, xlm, lxmert]'       )

import argparse

import torch

from transformers import BertConfig, BertForPreTraining, load_tf_weights_in_bert
from transformers.utils import logging


logging.set_verbosity_info()




def       _SCREAMING_SNAKE_CASE   (						_lowerCamelCase					:     Optional[int]		,    _lowerCamelCase					:     List[Any]		,    _lowerCamelCase					:     Optional[Any])   ->       Dict:






							'''simple docstring'''





							__UpperCamelCase      :  str       =   BertConfig.from_json_file(_lowerCamelCase)
							print(F'Building PyTorch model from configuration: {config}')
							__UpperCamelCase      :  Optional[Any]       =   BertForPreTraining(_lowerCamelCase)

							# Load weights from tf checkpoint
							load_tf_weights_in_bert(_lowerCamelCase		,    _lowerCamelCase		,    _lowerCamelCase)

							# Save pytorch-model
							print(F'Save PyTorch model to {pytorch_dump_path}')
							torch.save(model.state_dict()		,    _lowerCamelCase)


if __name__ == "__main__":
		lowercase    :   Union[str, Any]						=			argparse.ArgumentParser()
		# Required parameters
		parser.add_argument(
		    '--tf_checkpoint_path', default=None, type=str, required=True, help='Path to the TensorFlow checkpoint path.'
		)
		parser.add_argument(
		    '--bert_config_file',
		    default=None,
		    type=str,
		    required=True,
		    help=(
		        'The config json file corresponding to the pre-trained BERT model. \n'
		        'This specifies the model architecture.'
		    ),
		)
		parser.add_argument(
		    '--pytorch_dump_path', default=None, type=str, required=True, help='Path to the output PyTorch model.'
		)
		lowercase    :   Any						=			parser.parse_args()
		convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.bert_config_file, args.pytorch_dump_path)